# Understanding the effects of word-level linguistic annotations in under-resourced neural machine translation

arXiv: https://arxiv.org/abs/2401.16078 · HTML: https://mlsanity.com/paper/2401.16078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 29 Jan 2024.

## Builds on (most cited first, 18 of 18)

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- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Using the Output Embedding to Improve Language Models](https://mlsanity.com/paper/1608.05859/) (2016)
- [Edinburgh Neural Machine Translation Systems for WMT 16](https://mlsanity.com/paper/1606.02891/) (2016)
- [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019)
- [Universal Dependency Parsing from Scratch](https://mlsanity.com/paper/1901.10457/) (2019)
- [Linguistic Input Features Improve Neural Machine Translation](https://mlsanity.com/paper/1606.02892/) (2016)
- [A Tree-based Decoder for Neural Machine Translation](https://mlsanity.com/paper/1808.09374/) (2018)
- [The University of Edinburgh's Neural MT Systems for WMT17](https://mlsanity.com/paper/1708.00726/) (2017)
- [Predicting Target Language CCG Supertags Improves Neural Machine Translation](https://mlsanity.com/paper/1702.01147/) (2017)
- [UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging](https://mlsanity.com/paper/1908.06931/) (2019)
- [A Multifaceted Evaluation of Neural versus Phrase-Based Machine Translation for 9 Language Directions](https://mlsanity.com/paper/1701.02901/) (2017)
- [Modeling Target-Side Inflection in Neural Machine Translation](https://mlsanity.com/paper/1707.06012/) (2017)
- [Latent Part-of-Speech Sequences for Neural Machine Translation](https://mlsanity.com/paper/1908.11782/) (2019)
- [Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing](https://mlsanity.com/paper/1809.01854/) (2018)
- [Chunk-Based Bi-Scale Decoder for Neural Machine Translation](https://mlsanity.com/paper/1705.01452/) (2017)
- [CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology](https://mlsanity.com/paper/1907.10129/) (2019)

## Abstract (arXiv)

This paper studies the effects of word-level linguistic annotations in under-resourced neural machine translation, for which there is incomplete evidence in the literature. The study covers eight language pairs, different training corpus sizes, two architectures, and three types of annotation: dummy tags (with no linguistic information at all), part-of-speech tags, and morpho-syntactic description tags, which consist of part of speech and morphological features. These linguistic annotations are interleaved in the input or output streams as a single tag placed before each word. In order to measure the performance under each scenario, we use automatic evaluation metrics and perform automatic error classification. Our experiments show that, in general, source-language annotations are helpful and morpho-syntactic descriptions outperform part of speech for some language pairs. On the contrary, when words are annotated in the target language, part-of-speech tags systematically outperform morpho-syntactic description tags in terms of automatic evaluation metrics, even though the use of morpho-syntactic description tags improves the grammaticality of the output. We provide a detailed analysis of the reasons behind this result.